Personalized Health Care and Cancer Therapeutics (Biomarkers and Treatment)

April 17,, 2011

Dr Howard L. McLeodEshelman Distinguished Professor and Director

Institute for Pharmacogenomics and Individualized Therapy (IPIT)University of North Carolina – Chapel Hill, NC

“A surgeon who uses the wrong side of the scalpel cuts her own fingers and not the patient;

if the same applied to drugs they would have been investigated very carefully a long time ago”

Rudolph BucheimBeitrage zur Arzneimittellehre, 1849

Personalized medicine, schmersonalized medicine!

Medicine has always been personalized

Medicine is moving toward greater 'customer accountability'

Medicine will never be personalized

it is a change in expectation as well as some practical, process changes

Drivers of Personalized Medicine

Technology– Significant new opportunities over the past 5 years

Patient financial burden– When you are paying more, you want more say

Less personal care– Who will be my 'doctor' today?

Cost of care– Even the USA can't afford treating 100% to benefit 20%

Preemptive action is a clinical major weapon

Drug interactionsRenal dysfunctionAge

VaccinationAntimalarialTB

Mammographycolonoscopy

The clinical problem•Multiple active regimens for the treatment of most diseases•Variation in response to therapy•Unpredictable toxicity

With choice comes decision

$$$$$$$$$$$$$

Pharmacogenomic examples-2011• bcr/abl or 9:22 translocation—imatinib mesylate*• HER2-neu—trastuzumab**• C-kit mutations—imatinib mesylate**• Epidermal growth factor receptor mutations—gefitinib• Thiopurine S-methyltransferase—mercaptopurine and

azathioprine*• UGT1A1-irinotecan**• CYP2D9/VKORC1-warfarin*• HLA-B*5701-abacavir *

• HLA-B*1502-carbamazepine *

• CYP2C19-clopidogrel• Cytochrome P-450 (CYP) 2D6—5-HT3 receptor

antagonists, antidepressants, ADHD drugs, and codeine derivatives, tamoxifen*

What needs to be done to determine hope vs hype?

•Find the 'right' biomarkers

•Validate in robust datasets

•Apply them!

We do not know very much about drugsIrinotecan

cell membrane

Irinotecan

Irinotecan

SN-38

SN-38

SN-38TOP1

Cell Death

APC

SN-38G

ABCB1

CYP3A4

CYP3A5CES1

CES2

UGT1A1

CES1

CES2

ABCC2

ABCG2

ABCC1

ADPRT

TDP1

CDC45L

XRCC1

NFKB1

NPC

ABCB1

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Discovery Strategies

HapMap

Linkage

cases controls

Association

0

1

2

3

4

Gra

de

Model systems

Expression array

What needs to be done to determine hope vs hype?

•Find the 'right' biomarkers

•Validate in robust datasets

•Apply them!

Phase I

In vivo Mechanism

Phase II

Biomarkerassessment

Phase III

Biomarkervalidation

Correlative science: business as usual

2011 Estimated US Cancer Cases*

*Excludes basal and squamous cell skin cancers and in situ carcinomas except urinary bladder.Source: American Cancer Society, 2005.

Men710,040

Women662,870

32% Breast

12% Lung and bronchus

11% Colon and rectum

6% Uterine corpus

4% Non-Hodgkin lymphoma

4% Melanomaof skin

3% Ovary

3% Thyroid

2% Urinary bladder

2% Pancreas

21% All Other Sites

Prostate 33%

Lung and bronchus 13%

Colon and rectum 10%

Urinary bladder 7%

Melanoma of skin 5%

Non-Hodgkin 4%

lymphoma

Kidney 3%

Leukemia 3%

Oral Cavity 3%

Pancreas 2%

All Other Sites 17%

C90401; n=1020 C40101; n=4646

C80203/80405; n=2200 C80203/80405; n=2200

C50303; n=430C50303; n=430

C10105; MDS

C80303; n=528C80303; n=528

C80101 gastric; n=800

C30502; n=270 C30502; n=270

2010 Estimated US Cancer Cases*

*Excludes basal and squamous cell skin cancers and in situ carcinomas except urinary bladder.Source: American Cancer Society, 2005.

Men710,040

Women662,870

32% Breast

12% Lung and bronchus

11% Colon and rectum

6% Uterine corpus

4% Non-Hodgkin lymphoma

4% Melanomaof skin

3% Ovary

3% Thyroid

2% Urinary bladder

2% Pancreas

21% All Other Sites

Prostate 33%

Lung and bronchus 13%

Colon and rectum 10%

Urinary bladder 7%

Melanoma of skin 5%

Non-Hodgkin 4%

lymphoma

Kidney 3%

Leukemia 3%

Oral Cavity 3%

Pancreas 2%

All Other Sites 17%

C90401; n=1020 C40101; n=4646

C80203/80405; n=2200 C80203/80405; n=2200

C50303; n=430C50303; n=430

C10105; MDS

C80303; n=528C80303; n=528

C80101 gastric; n=800

C30502; n=270 C30502; n=270

GWAS x 2GWAS

NextGEN

GWAS

GWAS

What needs to be done to determine hope vs hype?

•Find the 'right' biomarkers

•Validate in robust datasets

•Apply them!

Fundamental questions

When is surgery enough?

Should we use chemotherapy?difficult to reverse practice

Which treatment should we use?toxicity-many 'equal' therapiesefficacydosage

DNA Chip Analysis

“Gene signature”

Assay result:• low- or high- risk group• probability of distant relapse

Tumor tissue Single value on

“Gene signature”

Relapse Hazard Score

When should we use chemotherapy?

Time (months)

Dis

tant

Rela

pse

-fre

e S

urv

ival

0 20 40 60 80

0.0

0.2

0.4

0.6

0.8

1.0

P-value = 0.0001

N = 20

N = 16

Good prognosis

Poor prognosis

Prediction of disease recurrence after surgery in Stage II colon cancer

Watch and wait Not Predisposed to relapse

patients with stage II disease

Treat with therapy

Colon Cancer Technical Feasibility

Development StudiesSurgery Alone

NSABP C-01/C-02 (n=270)

CCF (n = 765)

Selection of Final Gene List & Algorithm

Development Studies Surgery + 5FU/LV

NSABP C-04 (n=308)

NSABP C-06 (n=508)

Clinical Validation Study – Stage II Colon Cancer

QUASAR (n=1,436)

Test Prognosis and Treatment Benefit

Development and Validation of a Multi-Gene RT-PCR Colon Cancer Assay

Validation of Analytical Methods

• NSABP and CCF Collaborations - 761 genes studied in 1,851 patients to select genes which predict recurrence and/or differential 5FU/LV benefit

• Clinical Validation of final assay in a large, prospectively-designed independent study

p=0.004

0%

5%

10%

15%

20%

25%

30%

35%

0 10 20 30 40 50 60 70

Recurrence Score

Ris

k o

f re

curr

ence

at

3 ye

ars

QUASAR RESULTS: Colon Cancer Recurrence Score Predicts Recurrence Following Surgery

STROMALFAP

INHBABGN

CELL CYCLEKi-67

c-MYCMYBL2

REFERENCEATP5EGPX1PGK1UBB

VDAC2

GADD45B

RECURRENCE SCORECalculated from Tumor

Gene Expression

Prospectively-Defined Primary Analysis in Stage II Colon Cancer (n=711)

Group Risk (by Kaplan-Meier)

12% 18% 22%

Fundamental questions

When is surgery enough?

Should we use chemotherapy?difficult to reverse practice

Which treatment should we use?toxicity-many 'equal' therapiesefficacydosage

Proliferation MetastasisAngiogenesisApoptosis Resistance

Shc

PI3-K

RafMEKK-1

MEKMKK-7

JNKERK

Ras

mTOR

Grb2

AKT

Sos-1

The Epidermal Growth Factor Receptor Pathway

Retrospective studies supporting K-ras and lack of anti-EGFR response

Single agent panitumumab: N=208

K-Ras Mutation Wild-Type K-Ras

Amado RG, et al. J Clin Oncol. 2008;26:1626-1634.

Panitumumab registration trial

Mutations aplenty!

Your patient with stage III sigmoid mucin neg adenocarcinoma has mutations in KRAS, BRAF, FGFR3, and CDK4

WHAT DO YOU DO?

CancerOutcome

Lymph node status

Distant metastasis Surgical technique

Patient biology

Tumor biology

Access to care

Toxicity-riskGenotypes

SupportiveCareGenotypes

InfectionDefenseGenotypes

DiseaseGenotypes

Comprehensive optimization of patient care